SUMMARIZATION
Weekly Honeycomb latency trend narrative for stakeholders in Confluence
Every week, compares latency across key endpoints against the prior week, pulls exemplar traces for whatever regressed.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly scheduled run opens the comparison window
- ActionPull this week vs last week latency percentiles per endpointHoneycomb
- LogicFlag endpoints that regressed beyond tolerance
- ActionFetch exemplar slow-trace waterfalls for regressionsHoneycomb
- ActionWrite a stakeholder-readable trend narrativeOpenAI
- OutputPublish a dated trend report page to ConfluenceConfluence
What it does
Once a week it pulls latency percentiles for your key endpoints from Honeycomb, compares them against the previous week, and explains what moved. For anything that got meaningfully slower it fetches an exemplar trace and narrates the cause. The output is a stakeholder-readable trend report published in Confluence.
When to use it
Use it for recurring performance reviews where the audience is product, leadership, or account managers — people who care about 'is it getting faster or slower and why' but won't read query results. It gives them a durable, linkable page each week.
How it works
- 1A weekly scheduled trigger kicks off the comparison window.
- 2Honeycomb returns this week's and last week's latency percentiles per endpoint.
- 3A logic step flags endpoints that regressed beyond a tolerance and ignores normal jitter.
- 4For each regression, Honeycomb fetches an exemplar slow trace waterfall.
- 5OpenAI writes the report: what improved, what regressed, the size of each change, and a plain-language cause for the regressions.
- 6The report is published as a new dated page under a Confluence space.
Set it up
What you configure once, before turning it on.
- 1Connect HoneycombDistributed traces and queries.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect ConfluenceSpaces, pages, blueprints.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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